Overview

Dataset statistics

Number of variables35
Number of observations66940
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.5 MiB
Average record size in memory259.0 B

Variable types

Categorical23
Numeric9
Boolean3

Alerts

사고번호 has a high cardinality: 66940 distinct values High cardinality
시군구_소범주 has a high cardinality: 465 distinct values High cardinality
사고요일 is highly correlated with 주말여부High correlation
중상자수 is highly correlated with 경상자수 and 1 other fieldsHigh correlation
경상자수 is highly correlated with 중상자수 and 1 other fieldsHigh correlation
부상신고자수 is highly correlated with 대형사고여부High correlation
사고내용 is highly correlated with 사망자수 and 2 other fieldsHigh correlation
사망자수 is highly correlated with 사고내용 and 1 other fieldsHigh correlation
사고유형_대범주 is highly correlated with 사고유형_소범주 and 1 other fieldsHigh correlation
사고유형_소범주 is highly correlated with 사고유형_대범주 and 2 other fieldsHigh correlation
도로형태_대범주 is highly correlated with 도로형태_소범주High correlation
도로형태_소범주 is highly correlated with 도로형태_대범주 and 1 other fieldsHigh correlation
노면상태_대범주 is highly correlated with 노면상태_소범주High correlation
노면상태_소범주 is highly correlated with 노면상태_대범주 and 1 other fieldsHigh correlation
피해운전자차종 is highly correlated with 사고유형_대범주 and 1 other fieldsHigh correlation
피해운전자상해정도 is highly correlated with 사고내용 and 1 other fieldsHigh correlation
주말여부 is highly correlated with 사고요일High correlation
대형사고여부 is highly correlated with 중상자수 and 2 other fieldsHigh correlation
법규위반 is highly correlated with 사고유형_소범주 and 1 other fieldsHigh correlation
기상상태 is highly correlated with 노면상태_소범주High correlation
가해운전자상해정도 is highly correlated with 사고내용High correlation
사고번호 is uniformly distributed Uniform
사고번호 has unique values Unique
사고시각 has 2018 (3.0%) zeros Zeros
사고요일 has 9625 (14.4%) zeros Zeros
중상자수 has 50625 (75.6%) zeros Zeros
경상자수 has 17900 (26.7%) zeros Zeros
부상신고자수 has 60363 (90.2%) zeros Zeros

Reproduction

Analysis started2022-11-19 02:20:45.485447
Analysis finished2022-11-19 02:21:32.691703
Duration47.21 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

사고번호
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct66940
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size523.1 KiB
A2019010100100001
 
1
A2020042300100046
 
1
A2020042300100063
 
1
A2020042300100064
 
1
A2020042300100065
 
1
Other values (66935)
66935 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters1137980
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique66940 ?
Unique (%)100.0%

Sample

1st rowA2019010100100001
2nd rowA2019010100100002
3rd rowA2019010100100003
4th rowA2019010100100019
5th rowA2019010100100020

Common Values

ValueCountFrequency (%)
A20190101001000011
 
< 0.1%
A20200423001000461
 
< 0.1%
A20200423001000631
 
< 0.1%
A20200423001000641
 
< 0.1%
A20200423001000651
 
< 0.1%
A20200423001000661
 
< 0.1%
A20200423001000671
 
< 0.1%
A20200423001000681
 
< 0.1%
A20200423001000691
 
< 0.1%
A20200423001000701
 
< 0.1%
Other values (66930)66930
> 99.9%

Length

2022-11-19T11:21:32.793059image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a20190101001000011
 
< 0.1%
a20190101001002341
 
< 0.1%
a20190101001002331
 
< 0.1%
a20190101001000031
 
< 0.1%
a20190101001000191
 
< 0.1%
a20190101001000201
 
< 0.1%
a20190101001000211
 
< 0.1%
a20190101001000221
 
< 0.1%
a20190101001000231
 
< 0.1%
a20190101001000411
 
< 0.1%
Other values (66930)66930
> 99.9%

Most occurring characters

ValueCountFrequency (%)
0473167
41.6%
1185194
 
16.3%
2161834
 
14.2%
A66940
 
5.9%
960386
 
5.3%
339666
 
3.5%
435071
 
3.1%
533632
 
3.0%
629924
 
2.6%
726750
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1071040
94.1%
Uppercase Letter66940
 
5.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0473167
44.2%
1185194
 
17.3%
2161834
 
15.1%
960386
 
5.6%
339666
 
3.7%
435071
 
3.3%
533632
 
3.1%
629924
 
2.8%
726750
 
2.5%
825416
 
2.4%
Uppercase Letter
ValueCountFrequency (%)
A66940
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1071040
94.1%
Latin66940
 
5.9%

Most frequent character per script

Common
ValueCountFrequency (%)
0473167
44.2%
1185194
 
17.3%
2161834
 
15.1%
960386
 
5.6%
339666
 
3.7%
435071
 
3.3%
533632
 
3.1%
629924
 
2.8%
726750
 
2.5%
825416
 
2.4%
Latin
ValueCountFrequency (%)
A66940
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1137980
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0473167
41.6%
1185194
 
16.3%
2161834
 
14.2%
A66940
 
5.9%
960386
 
5.3%
339666
 
3.5%
435071
 
3.1%
533632
 
3.0%
629924
 
2.6%
726750
 
2.4%

사고년도
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size523.1 KiB
2019
35399 
2020
31541 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters267760
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
201935399
52.9%
202031541
47.1%

Length

2022-11-19T11:21:32.952166image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-19T11:21:33.109228image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
201935399
52.9%
202031541
47.1%

Most occurring characters

ValueCountFrequency (%)
298481
36.8%
098481
36.8%
135399
 
13.2%
935399
 
13.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number267760
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
298481
36.8%
098481
36.8%
135399
 
13.2%
935399
 
13.2%

Most occurring scripts

ValueCountFrequency (%)
Common267760
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
298481
36.8%
098481
36.8%
135399
 
13.2%
935399
 
13.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII267760
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
298481
36.8%
098481
36.8%
135399
 
13.2%
935399
 
13.2%

사고월
Real number (ℝ≥0)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.623946818
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size523.1 KiB
2022-11-19T11:21:33.261635image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.395981179
Coefficient of variation (CV)0.5126824342
Kurtosis-1.176971574
Mean6.623946818
Median Absolute Deviation (MAD)3
Skewness-0.05545335964
Sum443407
Variance11.53268817
MonotonicityNot monotonic
2022-11-19T11:21:33.408828image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
106068
9.1%
116004
9.0%
55955
8.9%
65846
8.7%
75822
8.7%
85701
8.5%
95613
8.4%
45439
8.1%
15237
7.8%
125235
7.8%
Other values (2)10020
15.0%
ValueCountFrequency (%)
15237
7.8%
24920
7.3%
35100
7.6%
45439
8.1%
55955
8.9%
65846
8.7%
75822
8.7%
85701
8.5%
95613
8.4%
106068
9.1%
ValueCountFrequency (%)
125235
7.8%
116004
9.0%
106068
9.1%
95613
8.4%
85701
8.5%
75822
8.7%
65846
8.7%
55955
8.9%
45439
8.1%
35100
7.6%

사고일
Real number (ℝ≥0)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.90470571
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size523.1 KiB
2022-11-19T11:21:33.564306image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.707485334
Coefficient of variation (CV)0.5474785572
Kurtosis-1.170247768
Mean15.90470571
Median Absolute Deviation (MAD)7
Skewness-0.008839538678
Sum1064661
Variance75.82030084
MonotonicityNot monotonic
2022-11-19T11:21:33.714956image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
182341
 
3.5%
232335
 
3.5%
192310
 
3.5%
242291
 
3.4%
82290
 
3.4%
112272
 
3.4%
172270
 
3.4%
252263
 
3.4%
152261
 
3.4%
142256
 
3.4%
Other values (21)44051
65.8%
ValueCountFrequency (%)
11898
2.8%
22095
3.1%
32056
3.1%
42168
3.2%
52123
3.2%
62058
3.1%
72245
3.4%
82290
3.4%
92174
3.2%
102219
3.3%
ValueCountFrequency (%)
311354
2.0%
302043
3.1%
292127
3.2%
282170
3.2%
272052
3.1%
262047
3.1%
252263
3.4%
242291
3.4%
232335
3.5%
222237
3.3%

사고시각
Real number (ℝ≥0)

ZEROS

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5634367842
Minimum0
Maximum0.9583333333
Zeros2018
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size523.1 KiB
2022-11-19T11:21:33.898375image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.04166666667
Q10.375
median0.5833333333
Q30.75
95-th percentile0.9166666667
Maximum0.9583333333
Range0.9583333333
Interquartile range (IQR)0.375

Descriptive statistics

Standard deviation0.253978583
Coefficient of variation (CV)0.4507667766
Kurtosis-0.5857274026
Mean0.5634367842
Median Absolute Deviation (MAD)0.1666666667
Skewness-0.4720509568
Sum37716.45833
Variance0.06450512063
MonotonicityNot monotonic
2022-11-19T11:21:34.057792image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0.754682
 
7.0%
0.70833333334170
 
6.2%
0.79166666674080
 
6.1%
0.66666666673991
 
6.0%
0.6253805
 
5.7%
0.54166666673596
 
5.4%
0.58333333333587
 
5.4%
0.53446
 
5.1%
0.83333333333357
 
5.0%
0.45833333333278
 
4.9%
Other values (14)28948
43.2%
ValueCountFrequency (%)
02018
3.0%
0.041666666671636
2.4%
0.083333333331169
 
1.7%
0.125900
 
1.3%
0.1666666667886
 
1.3%
0.20833333331179
 
1.8%
0.251374
2.1%
0.29166666671891
2.8%
0.33333333333179
4.7%
0.3753089
4.6%
ValueCountFrequency (%)
0.95833333332530
3.8%
0.91666666672843
4.2%
0.8753269
4.9%
0.83333333333357
5.0%
0.79166666674080
6.1%
0.754682
7.0%
0.70833333334170
6.2%
0.66666666673991
6.0%
0.6253805
5.7%
0.58333333333587
5.4%

사고요일
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.888362713
Minimum0
Maximum6
Zeros9625
Zeros (%)14.4%
Negative0
Negative (%)0.0%
Memory size523.1 KiB
2022-11-19T11:21:34.216695image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.927744885
Coefficient of variation (CV)0.6674178682
Kurtosis-1.185360148
Mean2.888362713
Median Absolute Deviation (MAD)2
Skewness0.02694390661
Sum193347
Variance3.71620034
MonotonicityNot monotonic
2022-11-19T11:21:34.331901image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
410813
16.2%
310033
15.0%
110032
15.0%
29782
14.6%
09625
14.4%
59530
14.2%
67125
10.6%
ValueCountFrequency (%)
09625
14.4%
110032
15.0%
29782
14.6%
310033
15.0%
410813
16.2%
59530
14.2%
67125
10.6%
ValueCountFrequency (%)
67125
10.6%
59530
14.2%
410813
16.2%
310033
15.0%
29782
14.6%
110032
15.0%
09625
14.4%
Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size523.1 KiB
강남구
6633 
송파구
5029 
영등포구
 
4289
서초구
 
4210
강서구
 
2995
Other values (21)
43784 

Length

Max length4
Median length3
Mean length3.104377054
Min length2

Characters and Unicode

Total characters207807
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강서구
2nd row구로구
3rd row서초구
4th row중구
5th row성동구

Common Values

ValueCountFrequency (%)
강남구6633
 
9.9%
송파구5029
 
7.5%
영등포구4289
 
6.4%
서초구4210
 
6.3%
강서구2995
 
4.5%
노원구2922
 
4.4%
동대문구2786
 
4.2%
중랑구2705
 
4.0%
성북구2526
 
3.8%
구로구2525
 
3.8%
Other values (16)30320
45.3%

Length

2022-11-19T11:21:34.487940image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강남구6633
 
9.9%
송파구5029
 
7.5%
영등포구4289
 
6.4%
서초구4210
 
6.3%
강서구2995
 
4.5%
노원구2922
 
4.4%
동대문구2786
 
4.2%
중랑구2705
 
4.0%
성북구2526
 
3.8%
구로구2525
 
3.8%
Other values (16)30320
45.3%

Most occurring characters

ValueCountFrequency (%)
69462
33.4%
14238
 
6.9%
9643
 
4.6%
9061
 
4.4%
6696
 
3.2%
6633
 
3.2%
5029
 
2.4%
5029
 
2.4%
4688
 
2.3%
4649
 
2.2%
Other values (29)72679
35.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter207807
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
69462
33.4%
14238
 
6.9%
9643
 
4.6%
9061
 
4.4%
6696
 
3.2%
6633
 
3.2%
5029
 
2.4%
5029
 
2.4%
4688
 
2.3%
4649
 
2.2%
Other values (29)72679
35.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul207807
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
69462
33.4%
14238
 
6.9%
9643
 
4.6%
9061
 
4.4%
6696
 
3.2%
6633
 
3.2%
5029
 
2.4%
5029
 
2.4%
4688
 
2.3%
4649
 
2.2%
Other values (29)72679
35.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul207807
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
69462
33.4%
14238
 
6.9%
9643
 
4.6%
9061
 
4.4%
6696
 
3.2%
6633
 
3.2%
5029
 
2.4%
5029
 
2.4%
4688
 
2.3%
4649
 
2.2%
Other values (29)72679
35.0%

시군구_소범주
Categorical

HIGH CARDINALITY

Distinct465
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size523.1 KiB
강남구 역삼동
 
1422
관악구 신림동
 
1306
서초구 서초동
 
1268
노원구 상계동
 
1240
강서구 화곡동
 
1121
Other values (460)
60583 

Length

Max length11
Median length7
Mean length7.313683896
Min length5

Characters and Unicode

Total characters489578
Distinct characters218
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row강서구 방화동
2nd row구로구 고척동
3rd row서초구 서초동
4th row중구 회현동2가
5th row성동구 행당동

Common Values

ValueCountFrequency (%)
강남구 역삼동1422
 
2.1%
관악구 신림동1306
 
2.0%
서초구 서초동1268
 
1.9%
노원구 상계동1240
 
1.9%
강서구 화곡동1121
 
1.7%
강남구 논현동1071
 
1.6%
구로구 구로동1047
 
1.6%
관악구 봉천동1016
 
1.5%
중랑구 면목동962
 
1.4%
양천구 목동931
 
1.4%
Other values (455)55556
83.0%

Length

2022-11-19T11:21:34.734702image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강남구6633
 
5.0%
송파구5029
 
3.8%
영등포구4289
 
3.2%
서초구4210
 
3.1%
강서구2995
 
2.2%
노원구2922
 
2.2%
동대문구2786
 
2.1%
중랑구2705
 
2.0%
성북구2526
 
1.9%
구로구2525
 
1.9%
Other values (478)97260
72.6%

Most occurring characters

ValueCountFrequency (%)
75625
 
15.4%
71188
 
14.5%
66940
 
13.7%
14687
 
3.0%
11026
 
2.3%
8762
 
1.8%
7736
 
1.6%
7665
 
1.6%
7045
 
1.4%
6966
 
1.4%
Other values (208)211938
43.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter416593
85.1%
Space Separator66940
 
13.7%
Decimal Number6045
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
75625
 
18.2%
71188
 
17.1%
14687
 
3.5%
11026
 
2.6%
8762
 
2.1%
7736
 
1.9%
7665
 
1.8%
7045
 
1.7%
6966
 
1.7%
6731
 
1.6%
Other values (199)199162
47.8%
Decimal Number
ValueCountFrequency (%)
11603
26.5%
21450
24.0%
3997
16.5%
4777
12.9%
5516
 
8.5%
6449
 
7.4%
7184
 
3.0%
869
 
1.1%
Space Separator
ValueCountFrequency (%)
66940
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul416593
85.1%
Common72985
 
14.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
75625
 
18.2%
71188
 
17.1%
14687
 
3.5%
11026
 
2.6%
8762
 
2.1%
7736
 
1.9%
7665
 
1.8%
7045
 
1.7%
6966
 
1.7%
6731
 
1.6%
Other values (199)199162
47.8%
Common
ValueCountFrequency (%)
66940
91.7%
11603
 
2.2%
21450
 
2.0%
3997
 
1.4%
4777
 
1.1%
5516
 
0.7%
6449
 
0.6%
7184
 
0.3%
869
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul416593
85.1%
ASCII72985
 
14.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
75625
 
18.2%
71188
 
17.1%
14687
 
3.5%
11026
 
2.6%
8762
 
2.1%
7736
 
1.9%
7665
 
1.8%
7045
 
1.7%
6966
 
1.7%
6731
 
1.6%
Other values (199)199162
47.8%
ASCII
ValueCountFrequency (%)
66940
91.7%
11603
 
2.2%
21450
 
2.0%
3997
 
1.4%
4777
 
1.1%
5516
 
0.7%
6449
 
0.6%
7184
 
0.3%
869
 
0.1%

사고내용
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size523.1 KiB
경상사고
45986 
중상사고
16283 
부상신고사고
 
4268
사망사고
 
403

Length

Max length6
Median length4
Mean length4.12751718
Min length4

Characters and Unicode

Total characters276296
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경상사고
2nd row경상사고
3rd row경상사고
4th row경상사고
5th row경상사고

Common Values

ValueCountFrequency (%)
경상사고45986
68.7%
중상사고16283
 
24.3%
부상신고사고4268
 
6.4%
사망사고403
 
0.6%

Length

2022-11-19T11:21:34.949800image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-19T11:21:35.147032image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
경상사고45986
68.7%
중상사고16283
 
24.3%
부상신고사고4268
 
6.4%
사망사고403
 
0.6%

Most occurring characters

ValueCountFrequency (%)
71208
25.8%
67343
24.4%
66537
24.1%
45986
16.6%
16283
 
5.9%
4268
 
1.5%
4268
 
1.5%
403
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter276296
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
71208
25.8%
67343
24.4%
66537
24.1%
45986
16.6%
16283
 
5.9%
4268
 
1.5%
4268
 
1.5%
403
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul276296
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
71208
25.8%
67343
24.4%
66537
24.1%
45986
16.6%
16283
 
5.9%
4268
 
1.5%
4268
 
1.5%
403
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul276296
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
71208
25.8%
67343
24.4%
66537
24.1%
45986
16.6%
16283
 
5.9%
4268
 
1.5%
4268
 
1.5%
403
 
0.1%

사망자수
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size523.1 KiB
0
66537 
1
 
402
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters66940
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
066537
99.4%
1402
 
0.6%
21
 
< 0.1%

Length

2022-11-19T11:21:35.343325image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-19T11:21:35.545985image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
066537
99.4%
1402
 
0.6%
21
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
066537
99.4%
1402
 
0.6%
21
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number66940
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
066537
99.4%
1402
 
0.6%
21
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common66940
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
066537
99.4%
1402
 
0.6%
21
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII66940
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
066537
99.4%
1402
 
0.6%
21
 
< 0.1%

중상자수
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2645802211
Minimum0
Maximum11
Zeros50625
Zeros (%)75.6%
Negative0
Negative (%)0.0%
Memory size523.1 KiB
2022-11-19T11:21:35.748298image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum11
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4994958498
Coefficient of variation (CV)1.887880537
Kurtosis11.90191183
Mean0.2645802211
Median Absolute Deviation (MAD)0
Skewness2.27850235
Sum17711
Variance0.249496104
MonotonicityNot monotonic
2022-11-19T11:21:35.958174image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
050625
75.6%
115192
 
22.7%
2936
 
1.4%
3137
 
0.2%
434
 
0.1%
58
 
< 0.1%
64
 
< 0.1%
82
 
< 0.1%
91
 
< 0.1%
111
 
< 0.1%
ValueCountFrequency (%)
050625
75.6%
115192
 
22.7%
2936
 
1.4%
3137
 
0.2%
434
 
0.1%
58
 
< 0.1%
64
 
< 0.1%
82
 
< 0.1%
91
 
< 0.1%
111
 
< 0.1%
ValueCountFrequency (%)
111
 
< 0.1%
91
 
< 0.1%
82
 
< 0.1%
64
 
< 0.1%
58
 
< 0.1%
434
 
0.1%
3137
 
0.2%
2936
 
1.4%
115192
 
22.7%
050625
75.6%

경상자수
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.002688975
Minimum0
Maximum41
Zeros17900
Zeros (%)26.7%
Negative0
Negative (%)0.0%
Memory size523.1 KiB
2022-11-19T11:21:36.219484image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum41
Range41
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9670432048
Coefficient of variation (CV)0.9644498231
Kurtosis78.52393101
Mean1.002688975
Median Absolute Deviation (MAD)0
Skewness4.20858312
Sum67120
Variance0.9351725599
MonotonicityNot monotonic
2022-11-19T11:21:36.750139image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
137260
55.7%
017900
26.7%
28150
 
12.2%
32270
 
3.4%
4798
 
1.2%
5301
 
0.4%
6115
 
0.2%
750
 
0.1%
834
 
0.1%
920
 
< 0.1%
Other values (12)42
 
0.1%
ValueCountFrequency (%)
017900
26.7%
137260
55.7%
28150
 
12.2%
32270
 
3.4%
4798
 
1.2%
5301
 
0.4%
6115
 
0.2%
750
 
0.1%
834
 
0.1%
920
 
< 0.1%
ValueCountFrequency (%)
411
 
< 0.1%
281
 
< 0.1%
271
 
< 0.1%
191
 
< 0.1%
171
 
< 0.1%
162
 
< 0.1%
151
 
< 0.1%
142
 
< 0.1%
136
< 0.1%
124
< 0.1%

부상신고자수
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1113235733
Minimum0
Maximum17
Zeros60363
Zeros (%)90.2%
Negative0
Negative (%)0.0%
Memory size523.1 KiB
2022-11-19T11:21:36.900859image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum17
Range17
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.382203501
Coefficient of variation (CV)3.433266553
Kurtosis161.0793703
Mean0.1113235733
Median Absolute Deviation (MAD)0
Skewness7.326259744
Sum7452
Variance0.1460795161
MonotonicityNot monotonic
2022-11-19T11:21:37.075758image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
060363
90.2%
15973
 
8.9%
2458
 
0.7%
398
 
0.1%
427
 
< 0.1%
57
 
< 0.1%
64
 
< 0.1%
73
 
< 0.1%
82
 
< 0.1%
91
 
< 0.1%
Other values (4)4
 
< 0.1%
ValueCountFrequency (%)
060363
90.2%
15973
 
8.9%
2458
 
0.7%
398
 
0.1%
427
 
< 0.1%
57
 
< 0.1%
64
 
< 0.1%
73
 
< 0.1%
82
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
171
 
< 0.1%
161
 
< 0.1%
131
 
< 0.1%
101
 
< 0.1%
91
 
< 0.1%
82
 
< 0.1%
73
 
< 0.1%
64
 
< 0.1%
57
 
< 0.1%
427
< 0.1%

사고유형_대범주
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size523.1 KiB
차대차
50952 
차대사람
15988 

Length

Max length4
Median length3
Mean length3.238840753
Min length3

Characters and Unicode

Total characters216808
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row차대사람
2nd row차대차
3rd row차대차
4th row차대차
5th row차대사람

Common Values

ValueCountFrequency (%)
차대차50952
76.1%
차대사람15988
 
23.9%

Length

2022-11-19T11:21:37.247751image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-19T11:21:37.433171image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
차대차50952
76.1%
차대사람15988
 
23.9%

Most occurring characters

ValueCountFrequency (%)
117892
54.4%
66940
30.9%
15988
 
7.4%
15988
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter216808
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
117892
54.4%
66940
30.9%
15988
 
7.4%
15988
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul216808
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
117892
54.4%
66940
30.9%
15988
 
7.4%
15988
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul216808
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
117892
54.4%
66940
30.9%
15988
 
7.4%
15988
 
7.4%

사고유형_소범주
Categorical

HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size523.1 KiB
차대차 - 측면충돌
23320 
차대차 - 기타
14445 
차대차 - 추돌
10468 
차대사람 - 기타
6674 
차대사람 - 횡단중
5728 
Other values (5)
6305 

Length

Max length17
Median length12
Mean length9.3452196
Min length8

Characters and Unicode

Total characters625569
Distinct characters30
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row차대사람 - 횡단중
2nd row차대차 - 추돌
3rd row차대차 - 기타
4th row차대차 - 측면충돌
5th row차대사람 - 횡단중

Common Values

ValueCountFrequency (%)
차대차 - 측면충돌23320
34.8%
차대차 - 기타14445
21.6%
차대차 - 추돌10468
15.6%
차대사람 - 기타6674
 
10.0%
차대사람 - 횡단중5728
 
8.6%
차대차 - 정면충돌1817
 
2.7%
차대사람 - 차도통행중1602
 
2.4%
차대사람 - 보도통행중1065
 
1.6%
차대사람 - 길가장자리구역통행중919
 
1.4%
차대차 - 후진중충돌902
 
1.3%

Length

2022-11-19T11:21:37.564450image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-19T11:21:37.786362image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
66940
33.3%
차대차50952
25.4%
측면충돌23320
 
11.6%
기타21119
 
10.5%
차대사람15988
 
8.0%
추돌10468
 
5.2%
횡단중5728
 
2.9%
정면충돌1817
 
0.9%
차도통행중1602
 
0.8%
보도통행중1065
 
0.5%
Other values (2)1821
 
0.9%

Most occurring characters

ValueCountFrequency (%)
133880
21.4%
119494
19.1%
66940
10.7%
-66940
10.7%
36507
 
5.8%
26039
 
4.2%
25137
 
4.0%
23320
 
3.7%
21119
 
3.4%
21119
 
3.4%
Other values (20)85074
13.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter424749
67.9%
Space Separator133880
 
21.4%
Dash Punctuation66940
 
10.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
119494
28.1%
66940
15.8%
36507
 
8.6%
26039
 
6.1%
25137
 
5.9%
23320
 
5.5%
21119
 
5.0%
21119
 
5.0%
15988
 
3.8%
15988
 
3.8%
Other values (18)53098
12.5%
Space Separator
ValueCountFrequency (%)
133880
100.0%
Dash Punctuation
ValueCountFrequency (%)
-66940
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul424749
67.9%
Common200820
32.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
119494
28.1%
66940
15.8%
36507
 
8.6%
26039
 
6.1%
25137
 
5.9%
23320
 
5.5%
21119
 
5.0%
21119
 
5.0%
15988
 
3.8%
15988
 
3.8%
Other values (18)53098
12.5%
Common
ValueCountFrequency (%)
133880
66.7%
-66940
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul424749
67.9%
ASCII200820
32.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
133880
66.7%
-66940
33.3%
Hangul
ValueCountFrequency (%)
119494
28.1%
66940
15.8%
36507
 
8.6%
26039
 
6.1%
25137
 
5.9%
23320
 
5.5%
21119
 
5.0%
21119
 
5.0%
15988
 
3.8%
15988
 
3.8%
Other values (18)53098
12.5%

도로형태_대범주
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size523.1 KiB
단일로
33555 
교차로
29737 
기타
3434 
주차장
 
214

Length

Max length3
Median length3
Mean length2.948700329
Min length2

Characters and Unicode

Total characters197386
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row교차로
2nd row단일로
3rd row기타
4th row단일로
5th row교차로

Common Values

ValueCountFrequency (%)
단일로33555
50.1%
교차로29737
44.4%
기타3434
 
5.1%
주차장214
 
0.3%

Length

2022-11-19T11:21:38.028849image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-19T11:21:38.195360image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
단일로33555
50.1%
교차로29737
44.4%
기타3434
 
5.1%
주차장214
 
0.3%

Most occurring characters

ValueCountFrequency (%)
63292
32.1%
33555
17.0%
33555
17.0%
29951
15.2%
29737
15.1%
3434
 
1.7%
3434
 
1.7%
214
 
0.1%
214
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter197386
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
63292
32.1%
33555
17.0%
33555
17.0%
29951
15.2%
29737
15.1%
3434
 
1.7%
3434
 
1.7%
214
 
0.1%
214
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul197386
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
63292
32.1%
33555
17.0%
33555
17.0%
29951
15.2%
29737
15.1%
3434
 
1.7%
3434
 
1.7%
214
 
0.1%
214
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul197386
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
63292
32.1%
33555
17.0%
33555
17.0%
29951
15.2%
29737
15.1%
3434
 
1.7%
3434
 
1.7%
214
 
0.1%
214
 
0.1%

도로형태_소범주
Categorical

HIGH CORRELATION

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size523.1 KiB
단일로 - 기타
31941 
교차로 - 교차로안
17473 
교차로 - 교차로부근
9661 
기타 - 기타
3417 
교차로 - 교차로횡단보도내
 
2603
Other values (6)
 
1845

Length

Max length15
Median length14
Mean length9.220914252
Min length7

Characters and Unicode

Total characters617248
Distinct characters31
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row교차로 - 교차로횡단보도내
2nd row단일로 - 기타
3rd row기타 - 기타
4th row단일로 - 터널
5th row교차로 - 교차로부근

Common Values

ValueCountFrequency (%)
단일로 - 기타31941
47.7%
교차로 - 교차로안17473
26.1%
교차로 - 교차로부근9661
 
14.4%
기타 - 기타3417
 
5.1%
교차로 - 교차로횡단보도내2603
 
3.9%
단일로 - 지하차도(도로)내663
 
1.0%
단일로 - 교량517
 
0.8%
단일로 - 고가도로위242
 
0.4%
주차장 - 주차장214
 
0.3%
단일로 - 터널192
 
0.3%

Length

2022-11-19T11:21:38.332372image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
66940
33.3%
기타38775
19.3%
단일로33555
16.7%
교차로29737
14.8%
교차로안17473
 
8.7%
교차로부근9661
 
4.8%
교차로횡단보도내2603
 
1.3%
지하차도(도로)내663
 
0.3%
교량517
 
0.3%
주차장428
 
0.2%
Other values (3)468
 
0.2%

Most occurring characters

ValueCountFrequency (%)
133880
21.7%
93934
15.2%
-66940
10.8%
60565
9.8%
59991
9.7%
38775
 
6.3%
38775
 
6.3%
36158
 
5.9%
33555
 
5.4%
17473
 
2.8%
Other values (21)37202
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter415102
67.3%
Space Separator133880
 
21.7%
Dash Punctuation66940
 
10.8%
Open Punctuation663
 
0.1%
Close Punctuation663
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
93934
22.6%
60565
14.6%
59991
14.5%
38775
9.3%
38775
9.3%
36158
 
8.7%
33555
 
8.1%
17473
 
4.2%
9661
 
2.3%
9661
 
2.3%
Other values (17)16554
 
4.0%
Space Separator
ValueCountFrequency (%)
133880
100.0%
Dash Punctuation
ValueCountFrequency (%)
-66940
100.0%
Open Punctuation
ValueCountFrequency (%)
(663
100.0%
Close Punctuation
ValueCountFrequency (%)
)663
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul415102
67.3%
Common202146
32.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
93934
22.6%
60565
14.6%
59991
14.5%
38775
9.3%
38775
9.3%
36158
 
8.7%
33555
 
8.1%
17473
 
4.2%
9661
 
2.3%
9661
 
2.3%
Other values (17)16554
 
4.0%
Common
ValueCountFrequency (%)
133880
66.2%
-66940
33.1%
(663
 
0.3%
)663
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul415102
67.3%
ASCII202146
32.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
133880
66.2%
-66940
33.1%
(663
 
0.3%
)663
 
0.3%
Hangul
ValueCountFrequency (%)
93934
22.6%
60565
14.6%
59991
14.5%
38775
9.3%
38775
9.3%
36158
 
8.7%
33555
 
8.1%
17473
 
4.2%
9661
 
2.3%
9661
 
2.3%
Other values (17)16554
 
4.0%

노면상태_대범주
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size523.1 KiB
포장
66892 
비포장
 
48

Length

Max length3
Median length2
Mean length2.00071706
Min length2

Characters and Unicode

Total characters133928
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row포장
2nd row포장
3rd row포장
4th row포장
5th row포장

Common Values

ValueCountFrequency (%)
포장66892
99.9%
비포장48
 
0.1%

Length

2022-11-19T11:21:38.539462image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-19T11:21:38.675328image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
포장66892
99.9%
비포장48
 
0.1%

Most occurring characters

ValueCountFrequency (%)
66940
50.0%
66940
50.0%
48
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter133928
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
66940
50.0%
66940
50.0%
48
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul133928
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
66940
50.0%
66940
50.0%
48
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul133928
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
66940
50.0%
66940
50.0%
48
 
< 0.1%

노면상태_소범주
Categorical

HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size523.1 KiB
포장 - 건조
60589 
포장 - 젖음/습기
 
5410
포장 - 기타
 
809
포장 - 서리/결빙
 
46
포장 - 적설
 
35
Other values (5)
 
51

Length

Max length11
Median length7
Mean length7.246354945
Min length7

Characters and Unicode

Total characters485071
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row포장 - 건조
2nd row포장 - 건조
3rd row포장 - 건조
4th row포장 - 건조
5th row포장 - 건조

Common Values

ValueCountFrequency (%)
포장 - 건조60589
90.5%
포장 - 젖음/습기5410
 
8.1%
포장 - 기타809
 
1.2%
포장 - 서리/결빙46
 
0.1%
포장 - 적설35
 
0.1%
비포장 - 젖음/습기25
 
< 0.1%
비포장 - 건조19
 
< 0.1%
비포장 - 기타4
 
< 0.1%
포장 - 해빙2
 
< 0.1%
포장 - 침수1
 
< 0.1%

Length

2022-11-19T11:21:38.834090image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-19T11:21:39.162638image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
66940
33.3%
포장66892
33.3%
건조60608
30.2%
젖음/습기5435
 
2.7%
기타813
 
0.4%
비포장48
 
< 0.1%
서리/결빙46
 
< 0.1%
적설35
 
< 0.1%
해빙2
 
< 0.1%
침수1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
133880
27.6%
66940
13.8%
66940
13.8%
-66940
13.8%
60608
12.5%
60608
12.5%
6248
 
1.3%
/5481
 
1.1%
5435
 
1.1%
5435
 
1.1%
Other values (12)6556
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter278770
57.5%
Space Separator133880
27.6%
Dash Punctuation66940
 
13.8%
Other Punctuation5481
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
66940
24.0%
66940
24.0%
60608
21.7%
60608
21.7%
6248
 
2.2%
5435
 
1.9%
5435
 
1.9%
5435
 
1.9%
813
 
0.3%
48
 
< 0.1%
Other values (9)260
 
0.1%
Space Separator
ValueCountFrequency (%)
133880
100.0%
Dash Punctuation
ValueCountFrequency (%)
-66940
100.0%
Other Punctuation
ValueCountFrequency (%)
/5481
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul278770
57.5%
Common206301
42.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
66940
24.0%
66940
24.0%
60608
21.7%
60608
21.7%
6248
 
2.2%
5435
 
1.9%
5435
 
1.9%
5435
 
1.9%
813
 
0.3%
48
 
< 0.1%
Other values (9)260
 
0.1%
Common
ValueCountFrequency (%)
133880
64.9%
-66940
32.4%
/5481
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul278770
57.5%
ASCII206301
42.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
133880
64.9%
-66940
32.4%
/5481
 
2.7%
Hangul
ValueCountFrequency (%)
66940
24.0%
66940
24.0%
60608
21.7%
60608
21.7%
6248
 
2.2%
5435
 
1.9%
5435
 
1.9%
5435
 
1.9%
813
 
0.3%
48
 
< 0.1%
Other values (9)260
 
0.1%

법규위반
Categorical

HIGH CORRELATION

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size523.1 KiB
안전운전불이행
36380 
안전거리미확보
9607 
신호위반
8491 
보행자보호의무위반
 
2506
교차로운행방법위반
 
2501
Other values (6)
7455 

Length

Max length9
Median length7
Mean length6.495682701
Min length2

Characters and Unicode

Total characters434821
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row보행자보호의무위반
2nd row안전운전불이행
3rd row안전운전불이행
4th row안전운전불이행
5th row안전운전불이행

Common Values

ValueCountFrequency (%)
안전운전불이행36380
54.3%
안전거리미확보9607
 
14.4%
신호위반8491
 
12.7%
보행자보호의무위반2506
 
3.7%
교차로운행방법위반2501
 
3.7%
기타2251
 
3.4%
중앙선침범1896
 
2.8%
직진우회전진행방해1409
 
2.1%
차로위반1223
 
1.8%
불법유턴489
 
0.7%

Length

2022-11-19T11:21:39.431888image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
안전운전불이행36380
54.3%
안전거리미확보9607
 
14.4%
신호위반8491
 
12.7%
보행자보호의무위반2506
 
3.7%
교차로운행방법위반2501
 
3.7%
기타2251
 
3.4%
중앙선침범1896
 
2.8%
직진우회전진행방해1409
 
2.1%
차로위반1223
 
1.8%
불법유턴489
 
0.7%

Most occurring characters

ValueCountFrequency (%)
83776
19.3%
45987
10.6%
42796
9.8%
38881
8.9%
36869
 
8.5%
36380
 
8.4%
14721
 
3.4%
14721
 
3.4%
14619
 
3.4%
10997
 
2.5%
Other values (29)95074
21.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter434821
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
83776
19.3%
45987
10.6%
42796
9.8%
38881
8.9%
36869
 
8.5%
36380
 
8.4%
14721
 
3.4%
14721
 
3.4%
14619
 
3.4%
10997
 
2.5%
Other values (29)95074
21.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul434821
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
83776
19.3%
45987
10.6%
42796
9.8%
38881
8.9%
36869
 
8.5%
36380
 
8.4%
14721
 
3.4%
14721
 
3.4%
14619
 
3.4%
10997
 
2.5%
Other values (29)95074
21.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul434821
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
83776
19.3%
45987
10.6%
42796
9.8%
38881
8.9%
36869
 
8.5%
36380
 
8.4%
14721
 
3.4%
14721
 
3.4%
14619
 
3.4%
10997
 
2.5%
Other values (29)95074
21.9%

기상상태
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size523.1 KiB
맑음
59991 
 
4221
흐림
 
2619
 
106
안개
 
3

Length

Max length2
Median length2
Mean length1.935360024
Min length1

Characters and Unicode

Total characters129553
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row맑음
2nd row맑음
3rd row맑음
4th row맑음
5th row맑음

Common Values

ValueCountFrequency (%)
맑음59991
89.6%
4221
 
6.3%
흐림2619
 
3.9%
106
 
0.2%
안개3
 
< 0.1%

Length

2022-11-19T11:21:39.630636image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-19T11:21:39.867657image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
맑음59991
89.6%
4221
 
6.3%
흐림2619
 
3.9%
106
 
0.2%
안개3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
59991
46.3%
59991
46.3%
4221
 
3.3%
2619
 
2.0%
2619
 
2.0%
106
 
0.1%
3
 
< 0.1%
3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter129553
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
59991
46.3%
59991
46.3%
4221
 
3.3%
2619
 
2.0%
2619
 
2.0%
106
 
0.1%
3
 
< 0.1%
3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul129553
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
59991
46.3%
59991
46.3%
4221
 
3.3%
2619
 
2.0%
2619
 
2.0%
106
 
0.1%
3
 
< 0.1%
3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul129553
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
59991
46.3%
59991
46.3%
4221
 
3.3%
2619
 
2.0%
2619
 
2.0%
106
 
0.1%
3
 
< 0.1%
3
 
< 0.1%
Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size523.1 KiB
승용
44382 
이륜
7309 
화물
5841 
승합
 
3838
자전거
 
3286
Other values (6)
 
2284

Length

Max length11
Median length2
Mean length2.145787272
Min length2

Characters and Unicode

Total characters143639
Distinct characters34
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row승용
2nd row이륜
3rd row승용
4th row승용
5th row승용

Common Values

ValueCountFrequency (%)
승용44382
66.3%
이륜7309
 
10.9%
화물5841
 
8.7%
승합3838
 
5.7%
자전거3286
 
4.9%
원동기1107
 
1.7%
건설기계554
 
0.8%
개인형이동수단(PM)456
 
0.7%
특수149
 
0.2%
사륜오토바이(ATV)17
 
< 0.1%

Length

2022-11-19T11:21:40.085185image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
승용44382
66.3%
이륜7309
 
10.9%
화물5841
 
8.7%
승합3838
 
5.7%
자전거3286
 
4.9%
원동기1107
 
1.7%
건설기계554
 
0.8%
개인형이동수단(pm456
 
0.7%
특수149
 
0.2%
사륜오토바이(atv17
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
48220
33.6%
44382
30.9%
7782
 
5.4%
7326
 
5.1%
5841
 
4.1%
5841
 
4.1%
3838
 
2.7%
3286
 
2.3%
3286
 
2.3%
3286
 
2.3%
Other values (24)10551
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter141730
98.7%
Uppercase Letter963
 
0.7%
Close Punctuation473
 
0.3%
Open Punctuation473
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
48220
34.0%
44382
31.3%
7782
 
5.5%
7326
 
5.2%
5841
 
4.1%
5841
 
4.1%
3838
 
2.7%
3286
 
2.3%
3286
 
2.3%
3286
 
2.3%
Other values (17)8642
 
6.1%
Uppercase Letter
ValueCountFrequency (%)
M456
47.4%
P456
47.4%
A17
 
1.8%
T17
 
1.8%
V17
 
1.8%
Close Punctuation
ValueCountFrequency (%)
)473
100.0%
Open Punctuation
ValueCountFrequency (%)
(473
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul141730
98.7%
Latin963
 
0.7%
Common946
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
48220
34.0%
44382
31.3%
7782
 
5.5%
7326
 
5.2%
5841
 
4.1%
5841
 
4.1%
3838
 
2.7%
3286
 
2.3%
3286
 
2.3%
3286
 
2.3%
Other values (17)8642
 
6.1%
Latin
ValueCountFrequency (%)
M456
47.4%
P456
47.4%
A17
 
1.8%
T17
 
1.8%
V17
 
1.8%
Common
ValueCountFrequency (%)
)473
50.0%
(473
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul141730
98.7%
ASCII1909
 
1.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
48220
34.0%
44382
31.3%
7782
 
5.5%
7326
 
5.2%
5841
 
4.1%
5841
 
4.1%
3838
 
2.7%
3286
 
2.3%
3286
 
2.3%
3286
 
2.3%
Other values (17)8642
 
6.1%
ASCII
ValueCountFrequency (%)
)473
24.8%
(473
24.8%
M456
23.9%
P456
23.9%
A17
 
0.9%
T17
 
0.9%
V17
 
0.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.5 KiB
True
54542 
False
12398 
ValueCountFrequency (%)
True54542
81.5%
False12398
 
18.5%
2022-11-19T11:21:40.288958image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

가해운전자연령
Real number (ℝ≥0)

Distinct61
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.15582611
Minimum20
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size523.1 KiB
2022-11-19T11:21:40.511951image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile22
Q135
median50
Q360
95-th percentile72
Maximum80
Range60
Interquartile range (IQR)25

Descriptive statistics

Standard deviation15.46394783
Coefficient of variation (CV)0.3211230931
Kurtosis-0.9840362461
Mean48.15582611
Median Absolute Deviation (MAD)12
Skewness-0.1076478634
Sum3223551
Variance239.1336826
MonotonicityNot monotonic
2022-11-19T11:21:40.751781image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202388
 
3.6%
591787
 
2.7%
601716
 
2.6%
581710
 
2.6%
571682
 
2.5%
611633
 
2.4%
561623
 
2.4%
621598
 
2.4%
551468
 
2.2%
631455
 
2.2%
Other values (51)49880
74.5%
ValueCountFrequency (%)
202388
3.6%
21455
 
0.7%
22607
 
0.9%
23735
 
1.1%
24822
 
1.2%
25937
 
1.4%
261058
1.6%
271141
1.7%
281184
1.8%
291182
1.8%
ValueCountFrequency (%)
80510
0.8%
79172
 
0.3%
78256
 
0.4%
77315
0.5%
76337
0.5%
75367
0.5%
74428
0.6%
73511
0.8%
72669
1.0%
71777
1.2%

가해운전자상해정도
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size523.1 KiB
상해없음
55779 
경상
6425 
부상신고
 
3141
중상
 
1515
사망
 
80

Length

Max length4
Median length4
Mean length3.760382432
Min length2

Characters and Unicode

Total characters251720
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row상해없음
2nd row상해없음
3rd row상해없음
4th row상해없음
5th row상해없음

Common Values

ValueCountFrequency (%)
상해없음55779
83.3%
경상6425
 
9.6%
부상신고3141
 
4.7%
중상1515
 
2.3%
사망80
 
0.1%

Length

2022-11-19T11:21:40.932857image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-19T11:21:41.263532image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
상해없음55779
83.3%
경상6425
 
9.6%
부상신고3141
 
4.7%
중상1515
 
2.3%
사망80
 
0.1%

Most occurring characters

ValueCountFrequency (%)
66860
26.6%
55779
22.2%
55779
22.2%
55779
22.2%
6425
 
2.6%
3141
 
1.2%
3141
 
1.2%
3141
 
1.2%
1515
 
0.6%
80
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter251720
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
66860
26.6%
55779
22.2%
55779
22.2%
55779
22.2%
6425
 
2.6%
3141
 
1.2%
3141
 
1.2%
3141
 
1.2%
1515
 
0.6%
80
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul251720
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
66860
26.6%
55779
22.2%
55779
22.2%
55779
22.2%
6425
 
2.6%
3141
 
1.2%
3141
 
1.2%
3141
 
1.2%
1515
 
0.6%
80
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul251720
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
66860
26.6%
55779
22.2%
55779
22.2%
55779
22.2%
6425
 
2.6%
3141
 
1.2%
3141
 
1.2%
3141
 
1.2%
1515
 
0.6%
80
 
< 0.1%

피해운전자차종
Categorical

HIGH CORRELATION

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size523.1 KiB
승용
31454 
보행자
15988 
이륜
9089 
자전거
 
2962
승합
 
2809
Other values (4)
4638 

Length

Max length4
Median length2
Mean length2.317403645
Min length2

Characters and Unicode

Total characters155127
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row보행자
2nd row승용
3rd row화물
4th row승용
5th row보행자

Common Values

ValueCountFrequency (%)
승용31454
47.0%
보행자15988
23.9%
이륜9089
 
13.6%
자전거2962
 
4.4%
승합2809
 
4.2%
화물2795
 
4.2%
원동기1187
 
1.8%
기타불명555
 
0.8%
특수101
 
0.2%

Length

2022-11-19T11:21:41.532336image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-19T11:21:41.868830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
승용31454
47.0%
보행자15988
23.9%
이륜9089
 
13.6%
자전거2962
 
4.4%
승합2809
 
4.2%
화물2795
 
4.2%
원동기1187
 
1.8%
기타불명555
 
0.8%
특수101
 
0.2%

Most occurring characters

ValueCountFrequency (%)
34263
22.1%
31454
20.3%
18950
12.2%
15988
10.3%
15988
10.3%
9089
 
5.9%
9089
 
5.9%
2962
 
1.9%
2962
 
1.9%
2809
 
1.8%
Other values (10)11573
 
7.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter155127
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
34263
22.1%
31454
20.3%
18950
12.2%
15988
10.3%
15988
10.3%
9089
 
5.9%
9089
 
5.9%
2962
 
1.9%
2962
 
1.9%
2809
 
1.8%
Other values (10)11573
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul155127
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
34263
22.1%
31454
20.3%
18950
12.2%
15988
10.3%
15988
10.3%
9089
 
5.9%
9089
 
5.9%
2962
 
1.9%
2962
 
1.9%
2809
 
1.8%
Other values (10)11573
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul155127
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
34263
22.1%
31454
20.3%
18950
12.2%
15988
10.3%
15988
10.3%
9089
 
5.9%
9089
 
5.9%
2962
 
1.9%
2962
 
1.9%
2809
 
1.8%
Other values (10)11573
 
7.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.5 KiB
True
49621 
False
17319 
ValueCountFrequency (%)
True49621
74.1%
False17319
 
25.9%
2022-11-19T11:21:42.230261image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

피해운전자연령
Real number (ℝ≥0)

Distinct80
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.48948312
Minimum1
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size523.1 KiB
2022-11-19T11:21:42.445053image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile21
Q132
median46
Q358
95-th percentile72
Maximum80
Range79
Interquartile range (IQR)26

Descriptive statistics

Standard deviation16.26866434
Coefficient of variation (CV)0.3576357263
Kurtosis-0.742769683
Mean45.48948312
Median Absolute Deviation (MAD)13
Skewness0.0249230641
Sum3045066
Variance264.6694393
MonotonicityNot monotonic
2022-11-19T11:21:42.745941image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
591466
 
2.2%
271456
 
2.2%
371440
 
2.2%
581431
 
2.1%
281409
 
2.1%
291405
 
2.1%
501404
 
2.1%
491400
 
2.1%
601390
 
2.1%
511386
 
2.1%
Other values (70)52753
78.8%
ValueCountFrequency (%)
15
 
< 0.1%
214
 
< 0.1%
329
 
< 0.1%
450
 
0.1%
549
 
0.1%
684
0.1%
799
0.1%
8142
0.2%
9136
0.2%
10129
0.2%
ValueCountFrequency (%)
80981
1.5%
79211
 
0.3%
78243
 
0.4%
77316
 
0.5%
76337
 
0.5%
75357
 
0.5%
74304
 
0.5%
73391
 
0.6%
72560
0.8%
71549
0.8%

피해운전자상해정도
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size523.1 KiB
경상
41364 
중상
13397 
상해없음
9100 
부상신고
 
2478
사망
 
301

Length

Max length4
Median length2
Mean length2.354884972
Min length2

Characters and Unicode

Total characters157636
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경상
2nd row경상
3rd row경상
4th row경상
5th row경상

Common Values

ValueCountFrequency (%)
경상41364
61.8%
중상13397
 
20.0%
상해없음9100
 
13.6%
부상신고2478
 
3.7%
사망301
 
0.4%
기타불명300
 
0.4%

Length

2022-11-19T11:21:43.075215image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-19T11:21:43.496555image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
경상41364
61.8%
중상13397
 
20.0%
상해없음9100
 
13.6%
부상신고2478
 
3.7%
사망301
 
0.4%
기타불명300
 
0.4%

Most occurring characters

ValueCountFrequency (%)
66339
42.1%
41364
26.2%
13397
 
8.5%
9100
 
5.8%
9100
 
5.8%
9100
 
5.8%
2478
 
1.6%
2478
 
1.6%
2478
 
1.6%
301
 
0.2%
Other values (5)1501
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter157636
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
66339
42.1%
41364
26.2%
13397
 
8.5%
9100
 
5.8%
9100
 
5.8%
9100
 
5.8%
2478
 
1.6%
2478
 
1.6%
2478
 
1.6%
301
 
0.2%
Other values (5)1501
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul157636
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
66339
42.1%
41364
26.2%
13397
 
8.5%
9100
 
5.8%
9100
 
5.8%
9100
 
5.8%
2478
 
1.6%
2478
 
1.6%
2478
 
1.6%
301
 
0.2%
Other values (5)1501
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul157636
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
66339
42.1%
41364
26.2%
13397
 
8.5%
9100
 
5.8%
9100
 
5.8%
9100
 
5.8%
2478
 
1.6%
2478
 
1.6%
2478
 
1.6%
301
 
0.2%
Other values (5)1501
 
1.0%

주말여부
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.5 KiB
False
50285 
True
16655 
ValueCountFrequency (%)
False50285
75.1%
True16655
 
24.9%
2022-11-19T11:21:43.641607image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

대형사고여부
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size523.1 KiB
0.0
66930 
1.0
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters200820
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.066930
> 99.9%
1.010
 
< 0.1%

Length

2022-11-19T11:21:43.788635image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-19T11:21:43.996370image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.066930
> 99.9%
1.010
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0133870
66.7%
.66940
33.3%
110
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number133880
66.7%
Other Punctuation66940
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0133870
> 99.9%
110
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.66940
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common200820
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0133870
66.7%
.66940
33.3%
110
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII200820
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0133870
66.7%
.66940
33.3%
110
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size523.1 KiB
0.0
66699 
1.0
 
241

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters200820
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.066699
99.6%
1.0241
 
0.4%

Length

2022-11-19T11:21:44.148403image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-19T11:21:44.314814image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.066699
99.6%
1.0241
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0133639
66.5%
.66940
33.3%
1241
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number133880
66.7%
Other Punctuation66940
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0133639
99.8%
1241
 
0.2%
Other Punctuation
ValueCountFrequency (%)
.66940
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common200820
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0133639
66.5%
.66940
33.3%
1241
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII200820
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0133639
66.5%
.66940
33.3%
1241
 
0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size523.1 KiB
0.0
62973 
1.0
 
3967

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters200820
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.062973
94.1%
1.03967
 
5.9%

Length

2022-11-19T11:21:44.462057image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-19T11:21:44.595932image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.062973
94.1%
1.03967
 
5.9%

Most occurring characters

ValueCountFrequency (%)
0129913
64.7%
.66940
33.3%
13967
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number133880
66.7%
Other Punctuation66940
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0129913
97.0%
13967
 
3.0%
Other Punctuation
ValueCountFrequency (%)
.66940
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common200820
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0129913
64.7%
.66940
33.3%
13967
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII200820
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0129913
64.7%
.66940
33.3%
13967
 
2.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size523.1 KiB
0.0
65931 
1.0
 
1009

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters200820
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.065931
98.5%
1.01009
 
1.5%

Length

2022-11-19T11:21:44.733315image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-19T11:21:44.883155image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.065931
98.5%
1.01009
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0132871
66.2%
.66940
33.3%
11009
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number133880
66.7%
Other Punctuation66940
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0132871
99.2%
11009
 
0.8%
Other Punctuation
ValueCountFrequency (%)
.66940
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common200820
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0132871
66.2%
.66940
33.3%
11009
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII200820
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0132871
66.2%
.66940
33.3%
11009
 
0.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size523.1 KiB
0.0
65647 
1.0
 
1293

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters200820
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.065647
98.1%
1.01293
 
1.9%

Length

2022-11-19T11:21:45.017102image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-19T11:21:45.198512image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.065647
98.1%
1.01293
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0132587
66.0%
.66940
33.3%
11293
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number133880
66.7%
Other Punctuation66940
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0132587
99.0%
11293
 
1.0%
Other Punctuation
ValueCountFrequency (%)
.66940
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common200820
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0132587
66.0%
.66940
33.3%
11293
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII200820
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0132587
66.0%
.66940
33.3%
11293
 
0.6%

Interactions

2022-11-19T11:21:28.281159image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:12.180062image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:14.632173image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:16.614549image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:18.628609image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:20.676323image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:22.786474image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:24.554661image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:26.452251image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:28.502093image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:12.428093image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:14.902548image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:16.834346image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:18.851008image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:20.892649image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:22.986770image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:24.729132image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:26.669246image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:28.707396image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:12.660330image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:15.141637image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:17.064973image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:19.110394image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:21.071574image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:23.176829image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:24.919131image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:26.921557image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:28.912893image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:12.848579image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:15.343480image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:17.276272image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:19.355184image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:21.238164image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:23.375804image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:25.107079image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:27.109311image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:29.341273image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:13.067763image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:15.548761image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:17.469171image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:19.595694image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:21.431376image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:23.576303image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:25.313448image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:27.291226image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:29.520267image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:13.263082image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:15.748614image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:17.659872image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:19.787382image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:21.608039image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:23.776182image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:25.618154image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:27.488475image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:29.725804image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:13.464586image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:15.936342image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:17.875501image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:19.994715image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:21.994160image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:23.958820image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:25.818991image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:27.671295image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:29.952033image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:13.786303image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:16.149797image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:18.105264image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:20.207865image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:22.278517image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:24.141752image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:26.059283image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:27.871605image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:30.161682image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:14.347656image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:16.392020image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:18.344974image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:20.443409image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:22.560486image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:24.362492image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:26.240933image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-19T11:21:28.075861image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-11-19T11:21:45.406667image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-19T11:21:45.797422image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-19T11:21:46.088595image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-19T11:21:46.391093image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-19T11:21:46.689103image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-19T11:21:47.068323image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-19T11:21:30.631316image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-19T11:21:32.079867image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

사고번호사고년도사고월사고일사고시각사고요일시군구_대범주시군구_소범주사고내용사망자수중상자수경상자수부상신고자수사고유형_대범주사고유형_소범주도로형태_대범주도로형태_소범주노면상태_대범주노면상태_소범주법규위반기상상태가해운전자차종가해운전자남성여부가해운전자연령가해운전자상해정도피해운전자차종피해운전자남성여부피해운전자연령피해운전자상해정도주말여부대형사고여부고속국도사고여부음주사고여부무면허사고여부뺑소니사고여부
0A20190101001000012019110.0000001강서구강서구 방화동경상사고0010차대사람차대사람 - 횡단중교차로교차로 - 교차로횡단보도내포장포장 - 건조보행자보호의무위반맑음승용True26상해없음보행자True40경상False0.00.00.00.00.0
1A20190101001000022019110.0000001구로구구로구 고척동경상사고0010차대차차대차 - 추돌단일로단일로 - 기타포장포장 - 건조안전운전불이행맑음이륜True23상해없음승용True71경상False0.00.00.00.00.0
2A20190101001000032019110.0000001서초구서초구 서초동경상사고0010차대차차대차 - 기타기타기타 - 기타포장포장 - 건조안전운전불이행맑음승용True33상해없음화물True51경상False0.00.00.00.00.0
3A20190101001000192019110.0416671중구중구 회현동2가경상사고0010차대차차대차 - 측면충돌단일로단일로 - 터널포장포장 - 건조안전운전불이행맑음승용True58상해없음승용True62경상False0.00.00.00.00.0
4A20190101001000202019110.0416671성동구성동구 행당동경상사고0010차대사람차대사람 - 횡단중교차로교차로 - 교차로부근포장포장 - 건조안전운전불이행맑음승용True30상해없음보행자True32경상False0.00.00.00.00.0
5A20190101001000212019110.0416671송파구송파구 잠실동경상사고0040차대차차대차 - 추돌교차로교차로 - 교차로부근포장포장 - 건조안전운전불이행맑음승용True31상해없음승용True37경상False0.00.00.00.00.0
6A20190101001000222019110.0416671노원구노원구 공릉동경상사고0030차대차차대차 - 추돌단일로단일로 - 기타포장포장 - 건조안전운전불이행맑음승용True49상해없음승용True27경상False0.00.01.00.00.0
7A20190101001000232019110.0416671노원구노원구 상계동경상사고0050차대차차대차 - 추돌단일로단일로 - 기타포장포장 - 건조안전운전불이행맑음승용True29상해없음승용True47경상False0.00.01.00.00.0
8A20190101001000412019110.0833331강남구강남구 삼성동경상사고0020차대차차대차 - 기타교차로교차로 - 교차로안포장포장 - 건조안전운전불이행맑음승용False28상해없음승용True59경상False0.00.01.00.00.0
9A20190101001000422019110.0833331강남구강남구 논현동경상사고0010차대사람차대사람 - 기타단일로단일로 - 기타포장포장 - 건조안전운전불이행맑음승용False30상해없음보행자True47경상False0.00.01.00.00.0

Last rows

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66930A2020123100100500202012310.8333333서초구서초구 반포동경상사고0010차대차차대차 - 후진중충돌단일로단일로 - 지하차도(도로)내포장포장 - 건조안전운전불이행맑음승용True39상해없음승용False32경상False0.00.00.00.00.0
66931A2020123100100526202012310.8750003금천구금천구 시흥동경상사고0010차대차차대차 - 측면충돌교차로교차로 - 교차로안포장포장 - 건조기타맑음승용True27상해없음승용True50경상False0.00.00.00.01.0
66932A2020123100100528202012310.8750003강서구강서구 화곡동중상사고0100차대차차대차 - 기타교차로교차로 - 교차로부근포장포장 - 건조신호위반맑음원동기True20중상승용True52상해없음False0.00.00.00.00.0
66933A2020123100100529202012310.8750003강동구강동구 천호동경상사고0010차대차차대차 - 측면충돌단일로단일로 - 기타포장포장 - 건조안전거리미확보맑음승용True70상해없음승용True25경상False0.00.00.00.00.0
66934A2020123100100530202012310.8750003송파구송파구 잠실동중상사고0200차대차차대차 - 추돌단일로단일로 - 교량포장포장 - 건조안전거리미확보맑음승용True46상해없음승용False47중상False0.00.01.00.00.0
66935A2020123100100570202012310.9166673성북구성북구 보문동1가경상사고0010차대차차대차 - 측면충돌교차로교차로 - 교차로부근포장포장 - 건조안전운전불이행맑음이륜True24상해없음승용True54경상False0.00.00.00.00.0
66936A2020123100100571202012310.9166673동대문구동대문구 제기동경상사고0010차대차차대차 - 기타단일로단일로 - 기타포장포장 - 건조안전운전불이행맑음화물True35상해없음자전거True41경상False0.00.00.00.00.0
66937A2020123100100572202012310.9166673강동구강동구 강일동경상사고0010차대차차대차 - 정면충돌교차로교차로 - 교차로부근포장포장 - 건조중앙선침범흐림승용True61상해없음승용True21경상False0.00.00.00.00.0
66938A2020123100100592202012310.9583333송파구송파구 신천동중상사고0100차대차차대차 - 측면충돌단일로단일로 - 기타포장포장 - 건조불법유턴맑음승용True62상해없음이륜True22중상False0.00.00.00.00.0
66939A2020123100100593202012310.9583333양천구양천구 신월동경상사고0040차대차차대차 - 측면충돌단일로단일로 - 기타포장포장 - 건조안전운전불이행맑음승용True61상해없음승용True22경상False0.00.00.00.00.0